Evidence map›Paper›PMID 38028591›Full record

ArticleFrontiers in genetics2023

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.

Yu Yin, Congcong Chen, Dong Zhang, Qianguang Han, Zijie Wang, Zhengkai Huang, Hao Chen, Li Sun, Shuang Fei, Jun Tao and 4 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Yu Yin *Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Congcong Chen *Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Dong Zhang *Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Qianguang HanDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zijie WangDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhengkai HuangDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Hao ChenDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Li SunDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Shuang FeiDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jun TaoDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhijian HanDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ruoyun TanDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Min GuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiaobing JuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Nanjing Medical University · CNJiangsu Province Hospital · CNSecond Affiliated Hospital of Nanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

diagnosis modelfibrosisinterstitial fibrosis and tubular atrophykidney transplantationmachine learning algorithms

Identifiers

PMID38028591
PMCPMC10646529
OpenAlexW4388142203

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.